Tuberculosis (TB) is the leading cause of infectious disease deaths globally, recently surpassing HIV/AIDS. The WHO has set goals for the eventual elimination of TB, but there are few, if any, reliable tools to monitor progress toward this goal. Additionally our understanding of the spatial and demographic variability of TB transmission and incidence is inadequate, making the allocation of limited resources challenging. This project proposes to develop methods to estimate transmission and incidence of TB in real time using data sources that are widely available. In Aim 1, we will develop improved estimates of the serial interval and duration of disease at time of diagnosis by using existing data (ours and published). These estimates will make use of statistical methods and novel modifications of these approaches to make them relevant to TB. These estimates provide the foundation of monitoring TB, further modeling work to guide policy, and provide valuable insights into TB epidemiology. Aim 2 will focus on developing methods to estimate the reproductive number of TB in real time, providing insight on transmission patterns. We propose to modify methods we and others have developed for diseases with short latency to TB, relying on routinely collected surveillance data. We will extend these methods to estimate heterogeneities in the reproductive number spatially, across demographic characteristics, and according the presence/absence of drug resistance. In Aim 3 we address the challenge of estimating disease incidence. Current estimates are given on a countrywide basis and result from annual reports of newly diagnosed individuals supplied to the WHO by individual countries. This data does not provide granularity in incidence estimates and is hampered by substantial reporting issues, which are not well-understood. We propose a novel Bayesian approach to estimate TB incidence from TB prevalence surveys, using our estimates of duration derived in Aim 1. TB prevalence surveys are detailed cluster sampled surveys with spatial, clinical, and demographic information on all participants. They are performed throughout areas with a high burden of TB. This approach will provide more detailed information on TB incidence and its heterogeneities, allowing for more targeted allocation of resources. We will create R packages to disseminate the methods that we develop in all the aims for widespread use. This work will enable policy makers to better understand the incidence of TB as well as groups and areas that are carrying the highest burden of disease. This knowledge will guide intervention efforts, with the eventual goal of elimination.